Bayesian model selection in hydrogeophysics: Application to conceptual subsurface models of the South Oyster Bacterial Transport Site, Virginia, USA

Détails

ID Serval
serval:BIB_14F5DE1B59A9
Type
Article: article d'un périodique ou d'un magazine.
Sous-type
Compte-rendu: analyse d'une oeuvre publiée.
Collection
Publications
Titre
Bayesian model selection in hydrogeophysics: Application to conceptual subsurface models of the South Oyster Bacterial Transport Site, Virginia, USA
Périodique
Advances in Water Resources
Auteur(s)
Brunetti C., Linde N., Vrugt J.A.
ISSN
0309-1708
Statut éditorial
Publié
Date de publication
04/2017
Peer-reviewed
Oui
Volume
102
Pages
127-141
Langue
anglais
Résumé
Geophysical data can help to discriminate among multiple competing subsurface hypotheses (conceptual models). Here, we explore the merits of Bayesian model selection in hydrogeophysics using crosshole ground-penetrating radar data from the South Oyster Bacterial Transport Site in Virginia, USA. Implementation of Bayesian model selection requires computation of the marginal likelihood of the measured data, or evidence, for each conceptual model being used. In this paper, we compare three different evidence estimators, including (1) the brute force Monte Carlo method, (2) the Laplace-Metropolis method, and (3) the numerical integration method proposed by Volpi et al. (2016). The three types of subsurface models that we consider differ in their treatment of the porosity distribution and use (a) horizontal layering with fixed layer thicknesses, (b) vertical layering with fixed layer thicknesses and (c) a multi-Gaussian field. Our results demonstrate that all three estimators provide equivalent results in low parameter dimensions, yet in higher dimensions the brute force Monte Carlo method is inefficient. The isotropic multi-Gaussian model is most supported by the travel time data with Bayes factors that are larger than 10^100 compared to conceptual models that assume horizontal or vertical layering of the porosity field.
Mots-clé
Water Science and Technology
Web of science
Création de la notice
20/10/2017 13:53
Dernière modification de la notice
20/08/2019 12:43
Données d'usage